In retail, data-driven solutions require sophisticated deep learning models—models that are much more sophisticated than those offered by machine …

It is crucial that artificial intelligence and machine learning systems be accountable, fair, inclusive and transparent.

Banks and financial services institutions face increased competition not only from peer organizations within the industry, but also now from FinTech startups, Neobanks, and others. The way to compete is to deliver highly personalized services and innovative offerings. And increasingly, the way to do that is using AI/ML to derive data-driven insights upon which those services and offerings can be based. Financial services institutions increasingly have sought to use new data sources to expand their traditional risk analysis and make more personalized offerings to a larger customer base. Many have gone beyond traditional methods (e.g., using FICO scores, credit history, salary, and more) and developed new risk models and highly individualized creditworthiness ratings based on the analysis of additional data sources.

Reinforcement learning is an area of machine learning where an agent (e.g., a computer program) seeks to progress towards a reward (e.g., winning a …

… computing resources, applications plus artificial intelligence/machine learning-capable computation to scale to support these new workloads.”.

It seamlessly integrates classical machine learning libraries with quantum hardware and simulators, giving users the power to train quantum …

While neural networks seem to get all the glory, data is the unsung hero of AI projects – data lies at the heart of everything from model training to tuning to selection to validation. No matter how compelling the business case, or talented the team, without high-quality data, AI projects are doomed to fail. An example from the field of computer vision illustrates the challenge. While we marvel at the accuracy of image classification models such as vgg16 and ResNet[2], we may take it for granted that a database with over 14,000,000 hand-annotated images exists to train these models. These are hardly random images – rather, they are organized based on a similarly expansive effort called WordNet, an effort to build a lexical database for the English language started in 1985[3].

HPCwire: Walk us through the program, give us a sense of what these AI and science town halls are all about and what they are trying to accomplish? RS: If you remember back in 2007, we had three town hall meetings – at Argonne, Berkeley and Oak Ridge – that launched the whole DOE Exascale project and so forth. At that time the idea was to get people together and ask them, for exascale, what if we could build these faster machines, what would you do with them. It was a way to get people thinking about the possibility of that and of course it took long time to get the exascale computing program going. With these town halls we are kind of asking a variation on that question. Now we're asking the question of what's the opportunity for AI in science or the application of science, particularly in the context of DOE, but more broadly because DOE's got a lot of collaborations with NIH and other agencies. So really asking the fundamental question of what do we have to do in the AI space to make it relevant for science. The point of the town halls – three in the labs and one in Washington in October – is go get people thinking about what opportunities there are in different scientific domains for breakthrough science that can be accomplished by leveraging AI and working AI into simulation, and bringing AI into big data, bringing AI to the facility and so forth. So that's the concept; it's really to get the community moving.

According to findings from Hyperion Research, simulation is primarily responsible for expanding the global HPC market from $2 billion in 1990 to a projected $38 billion in 2022. And one of the fastest-growing components of that forecast is high performance data analysis -- using HPC systems for data-intensive simulation and analytics. HPC simulation began in government and academic research organizations, to tackle daunting problems in the "hard sciences": physics, chemistry, biology, astronomy/cosmology and geology. Even within academia, the use of HPC simulation now extends to disciplines including cultural anthropology and archeology, historical linguistics and the social sciences. The use of HPC systems primarily for integer-based, data-intensive computing, as opposed to floating point-based simulation, began in the intelligence/defense community in the 1960s, at the start of the supercomputer era, and spread to large investment banks in the financial services industry in the 1980s.

Fueled by GPUs, big data, and rapid advances in software, the AI revolution is upon us. Enterprises are re-tooling systems and exploring AI for everything from customer service to fraud surveillance to enhanced decision making. For IT organizations, deploying, managing and sustaining these environments is a significant challenge. In this article, we look at AI through the prism of workload and resource management and explain how new challenges are driving fresh innovation. AI resource management is an "all-of-the-above" challenge Building and deploying AI applications is a multi-stage workflow, and each stage involves different applications and frameworks with unique workload and resource management challenges. AI models are fueled by vast amounts of training data from sources that include SQL databases, NoSQL stores, and semi-structured data in object stores or data lakes.